Abstract

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Highlights

  • A Comprehensive Review on Sustainable Aspects of Big DataVinoth Kumar Ponnusamy 1 , Padmanathan Kasinathan 2,3 , Rajvikram Madurai Elavarasan 4 , Vinoth Ramanathan 5 , Ranjith Kumar Anandan 6 , Umashankar Subramaniam 7, * , Aritra Ghosh 8 and Eklas Hossain 9, *

  • The functional elements and predictive analyses that are performed in the smart grid yield knowledgeable insights through which better, faster and more informed decision-making can be achieved on the smart grid, as detailed under Section 3.1 [61,62]

  • With the literature analysis from the previous works related to big data analytics in smart grids, the concepts, requirements and technologies involved are highlighted

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Summary

A Comprehensive Review on Sustainable Aspects of Big Data

Vinoth Kumar Ponnusamy 1 , Padmanathan Kasinathan 2,3 , Rajvikram Madurai Elavarasan 4 , Vinoth Ramanathan 5 , Ranjith Kumar Anandan 6 , Umashankar Subramaniam 7, * , Aritra Ghosh 8 and Eklas Hossain 9, *. Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK;. Oregon Renewable Energy Center (OREC), Department of Electrical Engineering and Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA. R.; Ramanathan, V.; Anandan, R.K.; Subramaniam, U.; Ghosh, A.; Hossain, E. A Comprehensive Review on Sustainable Aspects of Big Data. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

Introduction conditions of the Creative Commons
Research Motivation
Motivation
Review Methodology
Literature Review
Background Methodology
Role of Big Data Analytics in the Smart Grid and Achieving Sustainable Goals
Smart Grid
Big Data from Smart Grid Operations
MV and LV Grid Planning
Asset Management
Voltage Regulation and Protection
Customer Operation
Field Services
Customer and Utility Operations
Regulatory Compliance
Asset and Workforce Management
Operation of the Assets
Asset Maintenance
System Control
Regulatory Reporting
Grid Operations
Field Service
Resource Planning
Scheduled Use of Assets
Role of Big Data Analytics in Smart Grids
Key Issues in Smart Grids and the Outcomes of Big Data Implementation
Big Data Analytics Process in the Smart Grid
Big Data Analytics’ Role in the Smart Grid to Achieve Sustainability
Objectives
Smart Grid Sustainability in Connection with the Achievement of SDGs
Big Data Analytics as Solutions to Achieve Sustainability in the Smart Grid
Challenges and Future Directions
Interoperability
Data Analytics Innovation
Data Privacy Innovation
Need for Standards and Regulatory Frameworks
Innovative Computational Analytics
Integration with Advanced Visualization
Findings
Conclusions
Full Text
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